Tag: Availability

OEE: Planned Downtime and Availability

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As a core metric, Overall Equipment Effectiveness or OEE has been adopted by many companies to improve operations and optimize the capacity of existing equipment.  Having completed several on site assessments over the past few months we have learned that almost all organizations are measuring performance and quality in real-time, however, the availability component of OEE is still a mystery and often misunderstood – specifically with regard to Set Up or Tool Changes.

We encourage you to review the detailed discussion of down time in our original posts “Calculating OEE – The Real OEE Formula With Examples” and “OEE, Down time, and TEEP” where we also present methods to calculate both OEE and TEEP.  The formula for Overall Equipment Effectiveness is simply stated as the product of three (3) elements:  Availability, Performance, and Quality.  Of these elements, availability presents the greatest opportunity for improvement.  This is certainly true for processes such as metal stamping, tube forming, and injection molding, to name a few, where tool changes are required to switch from one product or process to another.

Switch Time

Set up or change over time is defined as the amount of time required to change over the process from the last part produced to the first good part off the next process.  We have learned that confusion exists as to whether this is actually planned down time as it is an event that is known to occur and is absolutely required if we are going to make more than one product in a given machine.

Planned down time is not included in the Availability calculation.  As such, if change over time is considered as a planned event, the perceived availability would inherently improve as it would be excluded from the calculation.  Of course, the higher availability is just an illusion as the lost time was still incurred and the machine was not available to run production.

If we could change a process at the flip of a switch, set up time would be a non-issue and we could spend our time focusing on other improvement initiatives.  While some processes do require extensive change over time, there is always room for improvements.  This is best exemplified by the metal stamping industry where die changes literally went from Hours to Minutes.

To remain competitive and to increase the available capacity, many companies quickly adopted SMED (Single Minute Exchange of Dies) initiatives after recognizing that significant production capacity is being lost due to extensive change over times.  Overtime through extended shifts and capital for new equipment is also reduced as capacity utilization improves.

Significantly reduced inventories can also be realized as product change overs become less of a concern and also provide greater flexibility to accommodate changes in customer demand in real-time.  Significantly increased Inventory Turns will also be realized in conjunction with net available cash from operations.

Redefining Down Time

The return on investment for Quick Tool Change technologies is relatively short and the benefits are real and tangible as demonstrated through the metrics mentioned above.  Rather than attempt to categorize down time as either planned or unplanned, consider whether the activity being performed is impeding the normal production process or can be considered as an activity required for continuing production.

We prefer to classify down time as either direct or indirect.  Any down time such as Set Up, Material Changes, Equipment Breakdowns, Tooling Adjustments, or other activity that impedes production is considered DIRECT down time.  Indirect down time applies to events such as Preventive Maintenance, Company Meetings, or Scheduled IDLE Time.  These events are indeed PLANNED events where the machine or process is NOT scheduled to run.

Redefine the Objective

Set up or change over time is often the subject of much heated debate and tends to create more discussion than is necessary.  The reason for this is simple.  Corporate objectives are driven by metrics that measure performance to achieve a specific goal.

Unfortunately, in the latter case, the objectives are translated into personal performance concerns for those involved in the improvement process.  Rather than making real improvements, the tendency is to rationalize the current performance levels and to look for ways to revise the definition that creates the perception of poor performance. Since availability does not include planned down time, many attempts are made to exclude certain down time events, such as set up time, to create a better OEE result than was actually achieved.

Attempts to rationalize poor performance inhibits our ability to identify opportunities for improvement.  From a similar perspective, we should also be prudent with. and cognizant of, the time allotted for “planned” events.

It is for this reason that some companies have resorted to measuring TEEP based on a 24 hour day.  In many respects, TEEP eliminates all uncertainty with regard to availability since you are measured on the ability to produce a quality part at rate.  As such, our mission is simple – “To Safely Produce a Quality Part At Rate, Delivered On Time and In Full”.  Any activity that detracts from achieving or exceeding this mission is waste.

Remember to get your OEE spreadsheets at no charge from our Free Downloads Page or Free Downloads Box in the sidebar.  They can be easily and readily customized for your specific process or application.

Please feel free to send your comments, suggestions, or questions to Support@VergenceAnalytics.com

Until Next Time – STAY lean!

Vergence AnalyticsVergence Analytics
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OEE for Batch Processes

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We recently received an e-mail regarding OEE calculations for batch processes and more specifically the effect on down stream equipment that is directly dependent (perhaps integrated) on the batch process.  While the inquiry was specifically related to the printing industry, batch processing is found throughout manufacturing. Our more recent experiences pertain to heat treating operations where parts are loaded into a stationary fixed-load oven as opposed to a continuous belt process.

Batch processing will inherently cause directly integrated downstream equipment (such as cooling, quenching, or coating processes) to be idle. In many cases it doesn’t make sense to measure the OEE of each co-dependent piece of equipment that are part of the same line or process. Unless there is a strong case otherwise, it may be better to de-integrate or de-couple subsequent downstream processes.

Batch processing presents a myriad of challenges for line balancing, batch sizes, and capacity management in general.  We presented two articles in April 2009 that addressed the topic of  where OEE should be measured.  Click here for Part I or Click  here for Part II.

Scheduling Concerns – Theory of Constraints

Ideally, we want to measure OEE at the bottleneck operation.  When we apply the Theory of Constraints to our production process, we can assure that the flow of material is optimized through the whole system.  The key of course is to make sure that we have correctly identified the bottleneck operation.  In many cases this is the batch process.

While we are often challenged to balance our production operations, the real goal is to create a schedule that can be driven by demand.  Rather than build excess inventories of parts that aren’t required, we want to be able to synchronize our operations to produce on demand and as required to keep the bottleneck operation running.  Build only what is necessary:  the right part, the right quantity, at the right time.

Through my own experience, I have realized the greatest successes using the Theory of Constraints to establish our material flows and production scheduling strategy for batch processes.  Although an in-depth discussion is beyond the scope of this article, I highly recommend reading the following books that convey the concepts and application through a well written and uniquely entertaining style:

  1. In his book “The Goal“, Dr. Eliyahu A. Goldratt presents a unique story of a troubled plant and the steps they took to turn the operation around.
  2. Another book titled “Velocity“, from the AGI-Goldratt Institute and Jeff Cox also demonstrates how the Theory of Constraints and Lean Six Sigma can work together to bring operations to all new level of performance, efficiency, and effectiveness.

I am fond of the “fable” based story line presented by these books as it is allows you to create an image of the operation in your own mind while maintaining an objective view.  The analogies and references used in these books also serve as excellent instruction aids that can be used when teaching your own teams how the Theory of Constraints work.  We can quickly realize that the companies presented in either of the above books are not much different from our own.  As such, we are quickly pulled into the story to see what happens and how the journey unfolds as the story unfolds.

Please leave your comments regarding this or other topics.  We appreciate your feedback.  Also, remember to get your free OEE spreadsheets.  See our free downloads page or click on the file you want from the “Orange” box file on the sidebar.

Until Next Time – STAY lean!

Vergence AnalyticsVergence Analytics

Benchmarking OEE

Benchmarking Systems:

We have learned that an industry standard or definition for Overall Equipment Effectiveness (OEE) has been adopted by the Semi Conductor Industry and also confirms our approach to calculating and using OEE and other related metrics.

The SEMI standards of interest are as follows:

  • SEMI E10:  Definition and Measurement of Equipment Reliability, Availability, and Maintainability.
  • SEMI E35:  Guide to Calculate Cost of Ownership Metrics.
  • SEMI E58:  Reliability, Availability, and Maintainability Data Collection.
  • SEMI E79:  Definition and Measurement of Equipment Productivity – OEE Metrics.
  • SEMI E116:  Equipment Performance Tracking.
  • SEMI E124:  Definition and Calculation of Overall Factory Efficiency and other Factory-Level Productivity Metrics.

It is important to continually learn and improve our understanding regarding the development and application of metrics used in industry.  It is often said that you can’t believe everything you read (especially – on the internet).  As such, we recommend researching these standards to determine their applicability for your business as well.

Benchmarking Processes:

Best practices and methods used within and outside of your specific industry may bring a fresh perspective into the definition and policies that are already be in place in your organization.  Just as processes are subject to continual improvement, so are the systems that control them.  Although many companies use benchmarking data to establish their own performance metrics, we strongly encourage benchmarking of best practices or methods – this is where the real learning begins.

World Class OEE is typically defined as 85% or better.  Additionally, to achieve this level of “World Class Peformance” the factors for Availability, Performance, and Quality must be at least 90%, 95%, and 99.5% respectively.  While this data may present your team with a challenge, it does little to inspire real action.

Understanding the policies and methods used to measure performance coupled with an awareness of current best practices to achieve the desired levels of  performance will certainly provide a foundation for innovation and improvement.  It is significant to note that today’s most efficient and successful companies have all achieved levels of performance above and beyond their competition by understanding and benchmarking their competitors best practices.  With this data, the same companies went on to develop innovative best practices to outperform them.

A Practical Example

Availablity is typically presented as the greatest opportunity for improvement.  This is even suggested by the “World Class” levels stated above.  Further investigation usually points us to setup / adjustment or change over as one of the primary improvement opportunities.  Many articles and books have been written on Single Minute Exchange of Dies and other Quick Tool Change strategy, so it is not our intent to present them here.  The point here is that industry has identified this specific topic as a significant opportunity and in turn has provided significant documentation and varied approaches to improve setup time.

In the case of improving die changes a variety of techniques are used including:

  • Quick Locator Pins
  • Pre-Staged Tools
  • Rolling Bolsters
  • Sub-Plates
  • Programmable Controllers
  • Standard Pass Heights
  • Standard Shut Heights
  • Quarter Turn Clamps
  • Hydraulic Clamps
  • Magnetic Bolsters
  • Pre-Staged Material
  • Dual Coil De-Reelers
  • Scheduling Sequences
  • Change Over Teams versus Individual Effort
  • Standardized Changeover Procedures

As change over time becomes less of a factor for determining what parts to run and for how long, we can strive reduced inventories and improved preventive maintenance activities.

Today’s Challenge

The manufacturing community has been devastated by the recent economic downturn.  We are challenged to bring out the best of what we have while continuing to strive for process excellence in all facets of our business.

Remember to get your free Excel Templates by visiting our FREE Downloads page.  We appreciate your feedback.  Please leave a comment an email to leanexecution@gmail.com or vergence.consultin@gmail.com

Until Next Time – STAY Lean!

How OEE can improve your Inventory

Once you have established a robust OEE system, you should also be reaping benefits in other areas of your organization.

We will be offering some insights into the other performance metrics such as inventory over the next few weeks. Improved availability, performance, and quality will all have an impact on your inventory and materials management processes. Inventory turns is one metric that should be improving as your OEE improves. If not, perhaps there is an opportunity to integrate OEE even deeper into your organization.

In a truly lean organization, other vantage point metrics will provide evidence of a well integrated OEE system. Metrics such as delivery, quality (ppm), labour efficiency, lead time, mean time between failures, mean response times, down time, turn over, and financial performance indicators are all directly or indirectly affected by improvements to your operation and OEE.

We will discuss the impact of OEE on these “other” metrics over the next few posts. Remember, we also offer excel templates at no cost to you. Click on the “BOX” files on the sidebar to get your free templates today! Our templates offer more than a simple OEE calculator – they can be used immediately with little or no modifications to suit your processes.

Until next time, STAY lean!

Vergence – Lean Execution Team.

OEE Integration: Can you fix it?

As we are all aware, inspecting or measuring parts does not change the quality of the product.   Likewise, measuring and reporting OEE alone does not solve problems or improve performance.  While it is fair to say that increased focus and measurement of any process usually results in some degree of improvement, these are typically attributed to changes in human behavior due to observation and not necessarily real process improvements.

Using OEE to identify opportunities in your operation is the equivalent of turning the light on in a dark room.  Although the room hasn’t changed, we certainly have a better understanding of what it looks like.  As such, OEE is a vantage point metric that can be used to illuminate our understanding of the process and identify opportunities to drive improvements.

It is essential for your team to develop and utilize effective problem solving skills to successfully identify systemic and process root causes for failure and to develop and execute permanent corrective actions to resolve them.  Our experience suggests that the lack of solid and proven problem solving skills coupled with poor execution is the leading cause of failure for new initiatives such as OEE.

We introduced an approach to improving OEE in our “Improve OEE:  A Hands On Approach“, post (03-Jan-09).  Although we identified some of the tools that could be used to solve of the problems, we didn’t spend much time going into the details.  Over the next few posts, we’ll discuss some of the ideas in a little more detail.

The real problem for most companies is identifying what the real underlying root cause of the current “failure” mode is.  Without a good understanding of the root cause, the solutions developed and implemented will not be effective, only serving to temporarily cure the immediate superficial symptoms.

Using effective problem solving skills to analyze the OEE data and to develop and execute permanent corrective actions will assure sustainable and ever improving performance.

Until Next Time – STAY lean!

OEE Integration – Where do We Measure OEE? – Part I

OEE Integration Part IX – Where do we measure OEE?

Our recent posts have included numerous examples to calculate OEE correctly. We also discussed integration of OEE as an effective metric for managing your processes and ultimately how to analyze and use the data to improve your profitability.  We spent little time discussing where this measurement should occur.  OEE can be measured for both manual and automated lines as well as any stand alone operation.

The OEE factors (Performance, Availability, and Quality) are process output results.  The expectation, of course, is to manage the inputs to the process to assure the optimal result is achieved.  Availability, Performance, and Quality can be measured in real-time during production. However, the results should be subject to a due diligence review when production is complete.

At a minimum, it makes sense to measure OEE at the end (output) of the line or process but this is not always ideal.  The complexity of OEE measurement occurs when single or multiple sub-cells are constrained by an upstream or downstream operation or bottleneck operation.  The flow, rate, or pace of a process is always  restricted or limited by a  sequence / process constraint or bottleneck operation.  Just as a chain is only as strong as its weakest link, so too is the line speed limited by the bottleneck operation.

We contend that the “Control-Response” loop for any process must enable immediate and effective corrective action based on the measured data and observations.  Measuring OEE in real-time at the bottleneck process makes it an ideal “Trigger Point” metric or “Control-Response” metric for managing the overall process even in “isolation” at the bottleneck operation.  Any variations at the bottleneck correlate directly to upstream and downstream process performance.

A disruption to production flow may occur due to a stock-out condition or when a customer or supplier operation is down.  While these situations affect or impact the OEE Availability factor, external factors are beyond the scope of the immediate process.

Real-time OEE requires that these events and others, such as product disposition, are reported in real-time as well.  External events are more difficult to capture in real-time and by automated systems in particular.  Operator interfaces must accommodate reporting of these events as they occur.

Reporting PITFALL – After-the-Fact events

If a quality defect is discovered several days after reporting production and all parts are placed on hold for sorting or rework, the QUALITY Factor for that run should be changed to ZERO.  In turn, the net OEE for that run will also be ZERO.  If the system is not changed, the integrity of the data is lost.  This also exemplifies that real-time data can be deceiving if proper controls are not in place.

“Where do we measure?” is followed by “When do we measure?” The short list of examples provided here are likely events that are far and few between.  If this is a daily occurrence, consider adopting the banking policy of, “adjustments to your account will be reflected on the following business day”.  Your process / system is in need of a rapid fix.

OEE is one of the few vital signs or key performance metrics for your manufacturing operation.  As such, measure where you will reap the greatest benefit and focus your attention on the process or operation accordingly.  OEE is as much a diagnostic tool as it is a monitoring tool.

Until Next Time – STAY lean!

Vergence Analytics
Versalytics

OEE: Take the Hit

The simplicity of measuring and calculating OEE is compounded by the factors that ultimately influence the end result.  Because the concept of OEE can be readily embraced by most employees, it is easy for many people to get involved in the process of making improvements.

Unfortunately the variables involved with OEE, like those of many other measurement systems, fall under scrutiny.  The goal of achieving yet even higher OEE numbers is met with yet another review of the factors and how they are treated.  Usually the scope of this often heated discussion is focused on Availability.

The greatest task of all occurs when attempting to classify what qualifies as planned versus unplanned downtime.  Availability is the primary factor where significant improvements can be realized and is most certainly the focus of every TPM program in existence.  However, another significant factor that can greatly impact Availability is setup time.

We still receive questions and comments from our readers regarding setup time and whether or not they should “take the hit” for it.  We have met up with different rationale and reasoning to exclude setup time from the availability factor such as:  “We have all kinds of capacity and do the setups in our free time.” Or, “We do the setups on the off shift so the equipment is always ready when the first shift comes in.”

Regardless of the rationale, our short answer to the question of inclusion for setup time remains a simple, “Yes, take the hit.”  Before we get to much further let’s define what it is.  Setup time is typically defined as the time required to change or setup the next process.  The duration of time is measured from the last good part produced to the first good part produced from the new process.

Improving setup times provides for shorter runs, reduced inventories, increased available capacity, increased responsiveness, improved maintenance, and in turn, improved quality.  Shorter runs also provide the opportunity to maintain tools more effectively between runs as they are not as subject to excessive wear caused by longer run times and higher production levels.

Setup and Quick Die Change / Quick Tool Change

An exhaustive amount of work has been completed in many manufacturing disciplines to reduce and improve setup times.  Certainly, by simply ignoring the setup time, there is no real way to determine whether the new methods are having an impact unless another measurement system for setup is introduced.  We already have a measurement system in place, so why invent another one?

Quick Die Change and other Quick Tool Change strategies are common place in industries such as automotive stamping plants.  The objectives for Quick Die Change are attributed to LEAN principles such as single part flow and reduced inventories.  The benefits of these efforts, of course, extend to OEE and availability.

Setup and Production Sequencing

To exemplify the effect of sequencing and setup, consider a single tool that makes 8 variations of a product.  For the sake of discussion, let’s assume the only difference is the number of holes punched into the part.  The time for each punch removed from, or added to, the tool is the same.

The objective for scheduling this tool is quite obvious.  We need to minimize the number of punch changes to minimize the downtime.  If the parts required range from 1 hole to 8 holes, and we need 100 parts of each variant, we would arrange the schedule in such a manner as to make sure we are only adding one punch to the tool as we move on to the next variant.

In this case, setup time and sequencing are clearly a cause for concern and consideration.  Secondly, it is much easier to calculate the time required to run all the parts and how much capacity is required.  Including setup in the OEE factor also simplifies the calculation of overall capacity utilization for the piece of equipment in general.

In Conclusion

As we have stated in previous posts, the objective of measuring OEE is to identify opportunities for improvement.  Achieving higher numbers through the process of debate and elimating elements for consideration is not making improvements.  Don’t masquerade the problem or the opportunities. 

Setup is certainly one area where improvements can be measured and quantified.  Availability and OEE results provide an opportunity to demonstrate the effectiveness of these improvements accordingly.

If the leadership of the company is setting policy then the explanations for performance in this regard should be understood.  The only numbers that really matter are on the bottom line and hopefully they are black.

We would also encourage you to visit two of our recent posts, Improving OEE – A hands on approach (posted 03-Jan-09) and OEE and Availability, (posted 31-Dec-2008).

Until next time, stay LEAN.

OEE and Capacity Management

Capacity – Available or Required?

From a scheduling perspective it is very easy to determine how much capacity (or time) will be required to manufacture a minimum quantity of parts.  However, it is not just a matter of multiplying the Standard Cycle Time by the Quantity of Parts and dividing by the part or process OEE %.

As you may recall, the availability component of OEE also accounts for set up or change over time.  Unfortunately, change over time is not typically dependent on the quantity of parts to be produced.  As such, set up or change over time must be tracked / measured  for each individual process and treated separately.

For example, in the metal stamping industry, a die change may take 20 minutes from the last good part to the first “next” good part out.  The quantity produced is variable depending on the yield of the coil (material thickness versus weight), and the number of coils run.

The duration of the run is subject to the set up time and coil / material change over times.  For this reason, unlike Performance and Quality, Availability is not a constant.  From a scheduling perspective, we can calculate the minimum run time using factor based on (Performance X Quality) and then account for availability by adding the set up and material change over times.

If the scheduled quantity is FIXED,  then  we can likely use the simple equation as originally stated.  For example if a process is scheduled to produce 500 pieces of product A on a machine having a cycle time of 30 seconds and the OEE for the process is 85%, then the time to produce the parts would be calculated as follows:

  • (500 Parts X 30 Seconds) / 85% = 17647.1 seconds

In this example 4.2 hours at standard versus 4.9 hours based on the OEE index.  As we noted above, however, because the quantity of parts is FIXED, the set up time and / or change over time is less concerning.

Repeating this process for all the parts that run through a given machine, it is possible to determine the total capacity required to run production. 

Capacity Available

If you are considering new work for a piece of equipment or machinery, knowing how much capacity is available to run the work will eventually become part of the overall process.  Typically, an annual forecast is used to determine how many hours per year are required.  It is also possible that seasonal influences exist within your machine requirements, so perhaps a quarterly or even monthly capacity report is required.

To calculate the total capacity available, we can use the formula from our earlier example and simply adjust or change the volume accordingly based on the period being considered.  The available capacity is difference between the required capacity and planned operating capacity.

Capacity Considerations and OEE

As we have mentioned in previous posts, be cognizant of the variation that may be present in the data.  A company that has been running and collecting OEE data for several months or even years will certainly be able to scrutinize the integrity of the OEE index and determine it’s statistical relevance.

A PPI (process performance index) that considers both OEE and Throughput Variance will present a more statistically relevant method of approximating capacity utilization.

VARIATION is the top form of WASTE in any business.  Although understanding variance is important, of greater concern is eliminating the source(s) of variance.

Until next time – STAY lean!

Vergence Analytics

Improving OEE: A Hands On Approach

We have explored Overall Equipment Efficiency (OEE) from several perspectives and how it can be used as an effective performance metric.  The purpose of measuring and monitoring OEE, at a minimum, should be three fold:

  1. To ensure the current performance levels are sustained,
  2. To identify new opportunities for improvement,
  3. To assess the effectiveness of current improvement initiatives.

The Culture of Continuous Improvement and Innovation

A continuous improvement “mindset” must be part of the organizational culture to achieve maximum results.  Too many companies charge the engineering department or some other “arm” of the organization to generate the ideas that can be implemented to improve availability, performance, and / or quality.  We strongly urge you to include everyone in the improvement process, especially the very people who perform the tasks on a daily basis.  Why?  The simple answer is, “They are the eyes and ears of the process”.

Despite some of the old school thinking that may persist in industry, most people take pride in their work and want to do a good job.  OEE is as much a performance metric for the individuals on the shop floor as it is for the management and leadership of the company.  Even the most educated doctor will ask the patient what the symptoms are as part of the assessment process.

While it may be difficult to assess what level of improvement can be achieved, it has been suggested that world class OEE is 85%.  We suggest that you establish a reasonable baseline and determine relative improvements accordingly.  The baseline you use should be comprised of two key components:

  1. Historical data for OEE and each factor (Availability, Performance, and Quality)
  2. A detailed Standard Operating Procedure for each process under consideration

Getting Started – Collect and Communicate Data

Almost every continuous improvement (CI) activity or project is accompanied by a list of actions that must be implemented.  Where does this list come from?

There are at least two very basic approaches to getting the improvement process underway:

  1. Collect and analyze data from the current process
  2. Set up a FLIP Chart at the line or machine

Step 1 should be fairly straightforward.  The premise here is that OEE data is already being collected and analyzed on a regular basis.  Step 2 may not be as familiar to you.

FLIP Charts

This is probably one of the most fundamental and basic data collection tools available on the market.  This approach may seem overly simplistic but the objective is to keep it simple and effective.

Advantages:

  1. Data collection in “real time”
  2. Anyone can add to the List
  3. Anyone can update the List
  4. Readily Available to ALL
  5. Writing Skills ONLY
  6. Instant Feedback
  7. Highly Visible

What do we record on the FLIP chart?  We have experienced the best success with the following simple format.  At the top of the FLIP chart write down Today’s Date and Shift, then setup the following headings:

Time   Problem/Concern   Assigned To   Task Completed   By (Initials)

Any time an event occurs or an opportunity arises for improvement, simply enter the appropriate data under the headings shown.  The flip chart can also be used to track progress – INSTANTLY.  Whenever a task is completed, the person responsible for the “fix” simply enters the time / date and their initials.

FLIP Chart – Built in Accountability

Using the flip chart as a living “action item list” introduces accountability from all levels to the process on the shop floor.  As tasks or actions are completed, everyone will see that the concerns are being addressed causing the improvement cycle to continue and reinforcing the value of everyone’s input to the process.

Our experience has shown the FLIP chart to be one of the most engaging improvement processes on a continuing basis.  Improvement history is readily available on the shop floor.  No complex searches, computer programs, or advanced skill set is required to see what is going on and what is being done about it.  As much as we don’t like to put problems on display, you may be surprised how impressed your customers are with this type of interactive CI process.

The FLIP chart is a very primitive but effective tool for collecting data and communicating results.

Improving OEE

Since OEE is comprised of three elements, it stands to reason that at least three major improvement initiatives exist:  Availability, Performance, and Quality.  How do we go about improving these elements?

Availability: Start with a downtime assessment:

  1. Categorize Events (Planned vs. Unplanned)
  2. Frequency / Occurrence Rate
  3. Duration
  4. Type:  Planned, Preventable, Predictable, Unplanned, Unknown

From our previous discussions on Availability, the known “Planned” events may include such change events as materials, tooling, and personnel (shift changes and / or breaks).  Improving availability requires the elimination of UNPLANNED events and reducing the duration of PLANNED events.  Successful improvements can only be developed and achieved if there is integrity in the baseline information and data.

Implementing SMED (single minute exchange of dies) is one strategy to reduce the duration of die changes.  A detailed die change process is used to determine the activities that can be performed while the machine is still running (External Events) and those that can only be performed while the machine is down (Internal Events).  Further assessments are conducted to determine what improvements are possible to reduce the duration of the internal events.  Such improvements may include hydraulic clamping, quarter turn screws, standardized shut heights, standardized locating pins, standardized pass heights to name a few.

Scheduling sequences may also be an important factor in the change over process.  If a common material (type or color) is used for two different parts, it may be more effective to run them back to back through the same machine.  Tooling may be shared among different part numbers and would require less change over time if they were considered as a product family for scheduling purposes.

Policy changes and capital investments are easily justified when you are able to demonstrate the improvements using a “plan vs actual” strategy that is complimented by data and a standard operating procedure.

Performance: Improving performance is not to be confused with reducing the process time (making it faster).  They are two different activities entirely.  If the original cycle time or process rate was calculated correctly, then 100% performance should be achievable right?  Once again, the answer to this question depends on company policy and the method that was used to establish the standard.

Our purpose is not to introduce more confusion, but rather, to make sure that whatever policy is in place is clearly defined and understood.  Remember, the only real industry standard for OEE is the formula used to calculate the result:  A x P x Q.  A standard definition or criteria for determining the individual factors does not exist.

The cycle time for an automated process can easily be determined by measuring the output without disruption over a known period of time.  Is this consistent with company policy?  Is the standard cycle time based on the stated nameplate capacity (rate) or is it based on the actual achieved (optimum) cycle time?

A “button to button” cycle time may be established for a manual operation in a similar manner.  Although it may be perceived as a flaw, the button to button analysis may not necessarily consider container changes or restocking of components that may be required from time to time.  If these “other” tasks are not factored into the cycle time, then it would be impossible to achieve 100% performance unless someone other than the operator was made responsible for those activities.

Start with a Performance Assessment

  1. Confirm company policy and methods for calculating the cycle time.
  2. Confirm the Cycle Time or Production Rate (Time Study)
  3. Compare the Actual versus Standard Operating Procedure
  4. Review the process performance history and data records.
  5. Equipment Condition Assessment – Preventive Maintenance
  6. Process Type:  Automation, Semi-Automation, Manual (Human Effort)
  7. Confirm Reporting Integrity

Only after you have reviewed the data and discussed the opportunities with the team will you be able to develop a performance improvement plan.

Using the “button to button” manual process described above, we already indicated that a person other than the operator could be responsible for restocking components and changing containers to allow the operator to run the machine without interruption.  There may be other activities as well that could be performed someone other than the operator.  A detailed Standard Operating Procedure complete with clearly defined steps (step tasks) and timing for each is the best tool available to improve performance.

Is it possible to change the method or sequence of events that the operator is following to reduce the time taken to perform a step task.  Is the operation “handed”, in other words, does it favor right versus left handed people?  Is the material arranged in such a way as to optimize (minimize) the operator’s movements during the cycle?  Are all operator’s performing the step tasks per the standard operating procedure?  Is the machine itself performing at the optimized cycle or is it running at a slower speed due to electrical, mechanical, or fluid faults?

Some of the activities identified may result in speed increases that will lead to performance improvements relative to the current standard.  Again, company policy should dictate when and how standards are to be updated.  If the standard is updated everytime the cycle time is reduced, how will you recognize the improvement?  We would recommend resetting the standards annually in conjunction with the new fiscal year.  The new performance levels should also be reflected in the business plan.

Quality: This is perhaps one of the easiest to factors to define and may be one of the more difficult factors to improve.  Again this will depend on the definition or criteria used to calculate the Quality factor.  The typical definition adopted by most manufacturers states that any parts failing to meet First Time Through quality criteria include those designated as scrap, test, rework, sort, and / or hold.  In other words, First Time Through quality applies only to those parts that are considered acceptable at the point and time of production.

When do you start counting?  Should set up parts be included in the Quality definition?  We would argue against including set up parts in the quality calculation, however, that doesn’t mean they shouldn’t be accounted for because the material loss is a real cost to the company.  We would define set up time as starting from the last good part produced to the first good part produced for the next job in.

The objective of any Quality improvement strategy is obviously zero defects.  The task is getting it done.

Quality: Start with a Quality Assessment:

  1. Review Process Failure Modes Effects and Analysis (PFMEA)
  2. Review Current Quality Control Plans (Inspection Requirements)
  3. Review and Analyze Quality Performance Data
  4. Review scrap and rework analysis
  5. Identify Top Opportunities (Pareto Analysis)
  6. Initiate Problem Solving Activities (DMAIC, PDCA, PDSA, IDEA Loops)
  7. Execute problem solving strategy
  8. Update Lessons Learned and Best Practices

The ultimate goal for any quality program is to achieve a level of zero defects.  A second, closely related goal is to eliminate, reduce, and control variation in our processes.  Variation and defects are directly correlated and are typically quantified by statistical modeling tools such as the normal distribution or bell curve.  Many tools are available to study and analyze the various attributes of a process to effectively determine the root cause for a given defect.

Some of the many problem solving methods and tools include 8-Discipline Analysis, 5 Why, Fault Tree Analysis, Cause and Effect Diagrams, Pareto Analysis, Design of Experiments (DOE), Analysis of Variance (ANOVA) tools among others.

Next Steps

We have identified the various methods to generate improvement activities. The key to success is developing the action plans and executing them in a timely manner.  This is the critical part of the improvement process.

A word of caution:  Don’t confuse activity with action.  Too many times, the data collection and study processes consume all the resources and more time is spent on data presentation than real analysis.  The goal is to improve the process, solve the problems, and eliminate the defects.

No Input Change = No Output Change

Lessons Learned and Best Practices

It is possible that the wrong process was selected for the product being manufactured.  This may range from the actual tooling to the very equipment that is used to run it.  It is also possible that the capability of the machine was overstated or over-rated prior to purchase.

Maintaining a lessons learned database is one way to make sure that we don’t make the same mistake twice.  It can also serve as a future reference when developing standards for future products or processes.

Perhaps a product or process requires a technology that simply doesn’t exist.  Could this be the stepping stone for a future research and development project?  How do we take things to the next level – the break through?

Until next time – STAY lean!

Twitter:  @Versalytics

Please feel free to forward your questions or comments to us by e-mail at LeanExecution@gmail.com

OEE For Dedicated – Single Part – Processes

OEE For Dedicated – Single Part – Processes

Definition: 

Dedicated – Single Part – Process:  A process that produces a single product or slight variations on a theme and does not require significant tooling or equipment changeover events.

A single part process is the easiest application for a OEE pilot project.  The single part process also makes it easier to demonstrate some of the more advanced Lean Thinking tools that can be applied to improve your operation or process.  In our “Variation, Waste, and OEE” post, we introduced the potential impacts of variance to your organization.  We also restated our mission to control, reduce, and eliminate variation in our processes as the primary objective of LEAN.

We need to spend more time understanding what our true production capabilities are.  The single part process makes the process of understanding these principles much easier.  The lessons learned can then be applied to more complex or multipart processes.  In multipart or complex operations, production part sequencing may have a significant impact on hourly rates and overall shift throughput.  How would you know unless you actually had a model that provided the insight?

Process Velocity:  Measuring Throughput

Let’s start this discussion by asking a few simple questions that will help you to get your mind in gear.  Do you measure variation in production output?  Do you measure shift rates?  Do you use the “average” rate per hour to set up your production schedules?  How do you know when normal production rates have been achieved?  Does a high production rate on one shift really signify a process improvement or was it simply a statistically expected event?

Once again an example will best serve our discussion.  Assume the following data represents one week of production over three shifts:

Machine A:  Production Process Performance Report

Cycle Time (Seconds):   57      
Shift Standard (440 minutes) 440      
             
Day Shift Planned Quantity
Production Time Total Test Scrap Accept
Mon 1 440 420 1 2 417
Mon 2 440 390 1 1 388
Mon 3 440 320 1 3 316
Tue 1 440 361 1 1 359
Tue 2 440 392 1 5 386
Tue 3 440 365 1 2 362
Wed 1 440 402 1 7 394
Wed 2 440 317 1 6 310
Wed 3 440 430 1 1 428
Thu 1 440 453 1 5 447
Thu 2 440 419 1 3 415
Thu 3 440 366 1 1 364
Fri 1 440 400 1 2 397
Fri 2 440 411 1 4 406
Fri 3 440 379 1 2 376
Totals 15 6600 5825 15 45 5765

The following table is an extension of the above table and shows the unplanned downtime as well actual, standard, and ideal operating times.

Day Shift Unplanned Operating Time
Down Time Actual Standard Ideal
Mon 1 25 415.0 399.0 396.2
Mon 2 55 385.0 370.5 368.6
Mon 3 122 318.0 304.0 300.2
Tue 1 84 356.0 343.0 341.1
Tue 2 65 375.0 372.4 366.7
Tue 3 82 358.0 346.8 343.9
Wed 1 45 395.0 381.9 374.3
Wed 2 130 310.0 301.2 294.5
Wed 3 30 410.0 408.5 406.6
Thu 1 5 435.0 430.4 424.7
Thu 2 40 400.0 398.1 394.3
Thu 3 90 350.0 347.7 345.8
Fri 1 45 395.0 380.0 377.2
Fri 2 45 395.0 390.5 385.7
Fri 3 60 380.0 360.1 357.2
Totals 15 923 5677 5533.8 5476.8

The table below shows the OEE calculations for each day and shift worked.  Note that this table is also an extension of the above data.

Day Shift Overall Equipment Effectiveness (OEE)
Availability Performance Quality OEE
Mon 1 94.3% 96.1% 99.3% 90.0%
Mon 2 87.5% 96.2% 99.5% 83.8%
Mon 3 72.3% 95.6% 98.8% 68.2%
Tue 1 80.9% 96.3% 99.4% 77.5%
Tue 2 85.2% 99.3% 98.5% 83.3%
Tue 3 81.4% 96.9% 99.2% 78.2%
Wed 1 89.8% 96.7% 98.0% 85.1%
Wed 2 70.5% 97.1% 97.8% 66.9%
Wed 3 93.2% 99.6% 99.5% 92.4%
Thu 1 98.9% 98.9% 98.7% 96.5%
Thu 2 90.9% 99.5% 99.0% 89.6%
Thu 3 79.5% 99.3% 99.5% 78.6%
Fri 1 89.8% 96.2% 99.3% 85.7%
Fri 2 89.8% 98.8% 98.8% 87.7%
Fri 3 86.4% 94.8% 99.2% 81.2%
Totals 15 86.0% 97.5% 99.0% 83.0%

The results from the table above suggest that the process is running just short of world-class OEE (83% versus 90% for dedicated processes.  Note that 85% is considered world-class for multipart variable processes).  As you can see from the daily and shift results, a lot of variation is occurring over the course of the week.  This is the opportunity that we need to pursue further.  A quick scan of the data suggests that Wednesday 2nd shift and Monday 3rd shift are the main contributors to the reduced OEE.  We will investigate the data a little further to really understand what opportunities exist.

A dedicated, continuous process should yield a higher OEE since the process is not subject to continual setup and change over.  Although some model changes or variations to the existing product may exist, they are typically less disruptive.  A OEE of 90% may be an achievable target and is typical for most dedicated operations.

FREE Downloads

We are currently offering our Excel OEE Spreadsheet Templates and example files at no charge.  You can download our files from the ORANGE BOX on the sidebar titled “FREE DOWNLOADS” or click on the FREE Downloads Page.  These files can be used as is and can be easily modified to suit many different manufacturing processes.  There are no hidden files, formulas, or macros and no obligations for the services provided here.

Please forward your questions, comments, or suggestions to LeanExecution@gmail.com.  To request our services for a specific project, please send your inquiries to Vergence.Consulting@gmail.com.

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Until Next Time – STAY Lean!

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